Image registration is a challenging application in computer vision and image processing. It is encountered in fields such as remote sensing, biomedical imaging, data indexing and retrieval (e.g., digital libraries), surveillance, post-production (e.g., tracking, stereo reconstruction from multiple views), and the like. Many of these applications involve data from multiple modalities (e.g., biomedical imaging) that provide complementary information. However, in order to be properly used, an integration/combination/fusion step is called for. Various applications in computer vision require the extraction of specific structures of interest, namely the segmentation of the visual information. Despite the fact that these structures have similar origins, they can present certain degrees of variability.
The cardiac example in biomedical imaging is typical. The heart shape varies across age, gender, ethnicities and the like. Additionally, the cardiac shape can be corrupted by cardiovascular diseases. In medical image analysis, there is a strong need for image and shape alignment. The outcome of this process can be used as a clinical tool. Document image analysis and pattern recognition are also areas where shape registration and alignment are important. Here, the writing patterns differ between individuals although they refer to the same basis of letters.
The recognition problem may be appropriately solved if shapes are first aligned. The problem of registering geometric shapes is a complex issue in vision, graphics and medical imaging. It has been studied in various forms. A general registration formulation can be stated as: given two shapes, an input D and a target S, and a dissimilarity measure; find the best transformation that associates to any point of D a corresponding point at S and minimizes the dissimilarity measure between the transformed shape V and the target S. This dissimilarity can be defined either along the contour for shape-based techniques or in the entire region for area-based techniques, as determined by the contour. The shape registration problem can be separated from the shape recognition problem. In the recognition scenario, correspondences between the shapes can be considered known. Then the objective is to find from a given set of examples the shape that provides the lower dissimilarity measurement with the target. Alternatively, methods that do not require correspondence and are based on the comparison of some global shape characteristics have also been investigated for shape recognition.